PrusaMCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool does (tests orientations, ranks them, returns scores with explanations) but doesn't mention computational requirements, time complexity, whether it modifies the input file, or what happens with invalid inputs. It provides basic behavioral context but lacks operational details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences: the first explains what the tool does (tests and ranks orientations), the second explains what it returns. Every word contributes to understanding the tool's functionality without redundancy or unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations and no output schema, the description provides adequate context about what the tool does and returns, but lacks details about the ranking algorithm, score ranges, format of explanations, or error handling. Given the complexity of orientation analysis, more complete behavioral context would be helpful for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter has 100% schema description coverage, so the schema already documents it fully. The description doesn't add any parameter-specific information beyond what's in the schema, but with only one well-documented parameter, this is acceptable. The baseline for high schema coverage is 3, but the simplicity of a single parameter justifies a slightly higher score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: it tests 6 main orientations of a 3D model and ranks them based on specific criteria (overhangs, print height, bed contact, support volume). It distinguishes from siblings like 'analyze_mesh' or 'check_printability' by focusing specifically on orientation optimization rather than general analysis or printability checking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context (when you need to optimize 3D print orientation) but doesn't explicitly state when to use this tool versus alternatives like 'check_printability' or 'print_wizard'. No explicit exclusions or prerequisites are mentioned, leaving some ambiguity about when this specific orientation analysis is most appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.